CrossNet-VGA: Variational Collaboration and Graph Attention Fusion for Incomplete Multi-View Clustering

In recent years, multi-view data often suffer from incompleteness owing to environmental factors, equipment failures. Thus Incomplete Multi-View Clustering (IMVC) has become an important research focus, which aims to alleviate the adverse impacts of missing views and leverage inter-view complementary information to enhance clustering performance. However, existing IMVC methodologies suffer from three critical limitations: 1) Inadequate integration of cross-view learning and cross-instance learning; 2) Lack of explicit modeling for dynamic interactions between view-specific information and cross-view shared semantics; 3) Inability to dynamically capture high-order topological correlations under view-missing conditions, leading to semantic misalignment among samples. To address these challenges, we propose an IMVC framework CrossNet-VGA based on variational collaboration and graph attention fusion. Specifically, We formulate a novel multi-view evidence lower bound to explicitly separate view-specific latent variables and cross-view shared latent variables, and achieve inter-view semantic fusion by integrating variational distributions shared across views. Contrastive learning is employed to maximize mutual information and promote feature distribution uniformity, thereby achieving consistent representation learning. We employ dynamic $k$ -nearest neighbor graph construction and multi-head graph attention mechanisms to capture the inter-sample deep topological correlations, achieving robust structural alignment. Comprehensive experiments conducted on 6 public datasets demonstrate that CrossNet-VGA significantly outperforms the competing methods both on accuracy and robustness. The anonymous code of this work is available on GitHub at https://github.com/ggg2111/2025-TIP-CrossNet-VGA.